Lab 5 Part 2: ggplot customization

knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)

library(tidyverse)
library(here)

library(readxl)
library(gghighlight)
library(lubridate)
library(plotly)

Read in Excel file, wrangle the data

fish_noaa <- read_excel(here("data", "foss_landings.xlsx")) %>% 
  janitor::clean_names() %>% 
  mutate(across(where(is.character), tolower)) %>% 
  mutate(nmfs_name = str_sub(nmfs_name, end = -4)) %>% 
  filter(confidentiality == 'public')

fish_plot <- ggplot(data = fish_noaa,
                    aes(x = year, y = pounds, group = nmfs_name)) +
  geom_line(aes(color = nmfs_name)) +
  theme_minimal()

#make it interactive
ggplotly(fish_plot)

#highlight series based on conditions
ggplot(data = fish_noaa, aes(x = year, y = pounds, group = nmfs_name)) +
  geom_line() +
  gghighlight(nmfs_name == 'rockfishes') + #highlights just this group
  theme_minimal()

ggplot(data = fish_noaa, aes(x = year, y = pounds, group = nmfs_name)) +
  geom_line(aes(color = nmfs_name)) +
  gghighlight(max(pounds) > 1e8) + #highlights groups that match this criterion
  theme_minimal()

Read data from web

monrowe_wt <- read_csv('https://raw.githubusercontent.com/justinelang/esm244_w2023_lab5/master/data/MWTP_Elec_Daily.csv') %>% 
  janitor::clean_names()

monrowe_ts <- monrowe_wt %>% 
  mutate(date = lubridate::mdy(date)) %>% 
  mutate(record_month = lubridate::month(date)) %>% 
  mutate(month_name = month.abb[record_month]) %>% 
  mutate(month_name = fct_reorder(month_name, record_month))

ggplot(data = monrowe_ts, aes(x = month_name, y = total_kwh)) +
  geom_jitter(aes(color = month_name),
              show.legend = FALSE,
              alpha = .5,
              size = .3,
              width = 0.2) +
  theme_classic()